{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append(\"..\") # Adds higher directory to python modules path.\n",
    "from ta import *\n",
    "\n",
    "df = pd.read_csv('../data/datas.csv', sep=',')\n",
    "\n",
    "# Clean nan values\n",
    "df = utils.dropna(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_fast=12\n",
    "\n",
    "df['sma'] = df.Close.rolling(n_fast).mean()\n",
    "df['emafast1'] = df.Close.ewm(n_fast).mean()\n",
    "\n",
    "sma = df.Close.rolling(window=n_fast, min_periods=n_fast).mean()[:n_fast]\n",
    "rest = df.Close[n_fast:]\n",
    "df['emafast2'] = pd.concat([sma, rest]).ewm(span=n_fast, adjust=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119044668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "df[['Close', 'sma', 'emafast1', 'emafast2']].iloc[-20:,:].plot(figsize=(12, 10));\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>emafast1</th>\n",
       "      <th>emafast2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.00</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.76</td>\n",
       "      <td>5.875200</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.65</td>\n",
       "      <td>5.794051</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.97</td>\n",
       "      <td>5.843452</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.62</td>\n",
       "      <td>5.791337</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.56</td>\n",
       "      <td>5.744677</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.61</td>\n",
       "      <td>5.720526</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.65</td>\n",
       "      <td>5.709054</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5.52</td>\n",
       "      <td>5.680730</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.53</td>\n",
       "      <td>5.659682</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5.12</td>\n",
       "      <td>5.588767</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5.02</td>\n",
       "      <td>5.517892</td>\n",
       "      <td>5.584167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>5.00</td>\n",
       "      <td>5.456295</td>\n",
       "      <td>5.494295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5.24</td>\n",
       "      <td>5.431606</td>\n",
       "      <td>5.455173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>5.13</td>\n",
       "      <td>5.398415</td>\n",
       "      <td>5.405146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>4.82</td>\n",
       "      <td>5.336803</td>\n",
       "      <td>5.315124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>4.80</td>\n",
       "      <td>5.281267</td>\n",
       "      <td>5.235874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>4.87</td>\n",
       "      <td>5.239818</td>\n",
       "      <td>5.179586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>4.85</td>\n",
       "      <td>5.201446</td>\n",
       "      <td>5.128880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>4.87</td>\n",
       "      <td>5.169508</td>\n",
       "      <td>5.089052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>4.82</td>\n",
       "      <td>5.136471</td>\n",
       "      <td>5.047660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>4.81</td>\n",
       "      <td>5.106145</td>\n",
       "      <td>5.011097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>4.85</td>\n",
       "      <td>5.082726</td>\n",
       "      <td>4.986313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>4.82</td>\n",
       "      <td>5.059049</td>\n",
       "      <td>4.960726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>4.82</td>\n",
       "      <td>5.037786</td>\n",
       "      <td>4.939076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>4.92</td>\n",
       "      <td>5.027433</td>\n",
       "      <td>4.936141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>4.90</td>\n",
       "      <td>5.016355</td>\n",
       "      <td>4.930581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>4.92</td>\n",
       "      <td>5.008061</td>\n",
       "      <td>4.928953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>5.56</td>\n",
       "      <td>5.055138</td>\n",
       "      <td>5.026037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>5.55</td>\n",
       "      <td>5.096997</td>\n",
       "      <td>5.106647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52027</th>\n",
       "      <td>4014.21</td>\n",
       "      <td>4050.372768</td>\n",
       "      <td>4038.768486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52028</th>\n",
       "      <td>4042.04</td>\n",
       "      <td>4049.731785</td>\n",
       "      <td>4039.271796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52029</th>\n",
       "      <td>4062.00</td>\n",
       "      <td>4050.675494</td>\n",
       "      <td>4042.768443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52030</th>\n",
       "      <td>4061.02</td>\n",
       "      <td>4051.471225</td>\n",
       "      <td>4045.576375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52031</th>\n",
       "      <td>4031.00</td>\n",
       "      <td>4049.896516</td>\n",
       "      <td>4043.333855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52032</th>\n",
       "      <td>4029.05</td>\n",
       "      <td>4048.292938</td>\n",
       "      <td>4041.136339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52033</th>\n",
       "      <td>3997.97</td>\n",
       "      <td>4044.421942</td>\n",
       "      <td>4034.495364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52034</th>\n",
       "      <td>4016.02</td>\n",
       "      <td>4042.237178</td>\n",
       "      <td>4031.653000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52035</th>\n",
       "      <td>4004.99</td>\n",
       "      <td>4039.372010</td>\n",
       "      <td>4027.551000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52036</th>\n",
       "      <td>4002.26</td>\n",
       "      <td>4036.517240</td>\n",
       "      <td>4023.660077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52037</th>\n",
       "      <td>3988.01</td>\n",
       "      <td>4032.785914</td>\n",
       "      <td>4018.175450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52038</th>\n",
       "      <td>4000.06</td>\n",
       "      <td>4030.268536</td>\n",
       "      <td>4015.388458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52039</th>\n",
       "      <td>4014.21</td>\n",
       "      <td>4029.033264</td>\n",
       "      <td>4015.207156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52040</th>\n",
       "      <td>3999.00</td>\n",
       "      <td>4026.723013</td>\n",
       "      <td>4012.713748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52041</th>\n",
       "      <td>4015.41</td>\n",
       "      <td>4025.852781</td>\n",
       "      <td>4013.128556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52042</th>\n",
       "      <td>3997.41</td>\n",
       "      <td>4023.664875</td>\n",
       "      <td>4010.710316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52043</th>\n",
       "      <td>3987.52</td>\n",
       "      <td>4020.884500</td>\n",
       "      <td>4007.142575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52044</th>\n",
       "      <td>3879.54</td>\n",
       "      <td>4010.011846</td>\n",
       "      <td>3987.511410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52045</th>\n",
       "      <td>3845.39</td>\n",
       "      <td>3997.348627</td>\n",
       "      <td>3965.646578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52046</th>\n",
       "      <td>3872.98</td>\n",
       "      <td>3987.781810</td>\n",
       "      <td>3951.390181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52047</th>\n",
       "      <td>3835.10</td>\n",
       "      <td>3976.037055</td>\n",
       "      <td>3933.499384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52048</th>\n",
       "      <td>3645.99</td>\n",
       "      <td>3950.648820</td>\n",
       "      <td>3889.267171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52049</th>\n",
       "      <td>3750.59</td>\n",
       "      <td>3935.259680</td>\n",
       "      <td>3867.932222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52050</th>\n",
       "      <td>3799.79</td>\n",
       "      <td>3924.838935</td>\n",
       "      <td>3857.448803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52051</th>\n",
       "      <td>3797.00</td>\n",
       "      <td>3915.005171</td>\n",
       "      <td>3848.148987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52052</th>\n",
       "      <td>3795.25</td>\n",
       "      <td>3905.793235</td>\n",
       "      <td>3840.010681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52053</th>\n",
       "      <td>3922.85</td>\n",
       "      <td>3907.105294</td>\n",
       "      <td>3852.755192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52054</th>\n",
       "      <td>3879.04</td>\n",
       "      <td>3904.946425</td>\n",
       "      <td>3856.799009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52055</th>\n",
       "      <td>3899.00</td>\n",
       "      <td>3904.489008</td>\n",
       "      <td>3863.291469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52056</th>\n",
       "      <td>3934.21</td>\n",
       "      <td>3906.775238</td>\n",
       "      <td>3874.202012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>46306 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Close     emafast1     emafast2\n",
       "0         6.00     6.000000          NaN\n",
       "1         5.76     5.875200          NaN\n",
       "2         5.65     5.794051          NaN\n",
       "6         5.97     5.843452          NaN\n",
       "18        5.62     5.791337          NaN\n",
       "19        5.56     5.744677          NaN\n",
       "20        5.61     5.720526          NaN\n",
       "21        5.65     5.709054          NaN\n",
       "22        5.52     5.680730          NaN\n",
       "26        5.53     5.659682          NaN\n",
       "43        5.12     5.588767          NaN\n",
       "44        5.02     5.517892     5.584167\n",
       "45        5.00     5.456295     5.494295\n",
       "49        5.24     5.431606     5.455173\n",
       "50        5.13     5.398415     5.405146\n",
       "66        4.82     5.336803     5.315124\n",
       "69        4.80     5.281267     5.235874\n",
       "73        4.87     5.239818     5.179586\n",
       "75        4.85     5.201446     5.128880\n",
       "98        4.87     5.169508     5.089052\n",
       "117       4.82     5.136471     5.047660\n",
       "120       4.81     5.106145     5.011097\n",
       "123       4.85     5.082726     4.986313\n",
       "124       4.82     5.059049     4.960726\n",
       "126       4.82     5.037786     4.939076\n",
       "127       4.92     5.027433     4.936141\n",
       "144       4.90     5.016355     4.930581\n",
       "161       4.92     5.008061     4.928953\n",
       "163       5.56     5.055138     5.026037\n",
       "164       5.55     5.096997     5.106647\n",
       "...        ...          ...          ...\n",
       "52027  4014.21  4050.372768  4038.768486\n",
       "52028  4042.04  4049.731785  4039.271796\n",
       "52029  4062.00  4050.675494  4042.768443\n",
       "52030  4061.02  4051.471225  4045.576375\n",
       "52031  4031.00  4049.896516  4043.333855\n",
       "52032  4029.05  4048.292938  4041.136339\n",
       "52033  3997.97  4044.421942  4034.495364\n",
       "52034  4016.02  4042.237178  4031.653000\n",
       "52035  4004.99  4039.372010  4027.551000\n",
       "52036  4002.26  4036.517240  4023.660077\n",
       "52037  3988.01  4032.785914  4018.175450\n",
       "52038  4000.06  4030.268536  4015.388458\n",
       "52039  4014.21  4029.033264  4015.207156\n",
       "52040  3999.00  4026.723013  4012.713748\n",
       "52041  4015.41  4025.852781  4013.128556\n",
       "52042  3997.41  4023.664875  4010.710316\n",
       "52043  3987.52  4020.884500  4007.142575\n",
       "52044  3879.54  4010.011846  3987.511410\n",
       "52045  3845.39  3997.348627  3965.646578\n",
       "52046  3872.98  3987.781810  3951.390181\n",
       "52047  3835.10  3976.037055  3933.499384\n",
       "52048  3645.99  3950.648820  3889.267171\n",
       "52049  3750.59  3935.259680  3867.932222\n",
       "52050  3799.79  3924.838935  3857.448803\n",
       "52051  3797.00  3915.005171  3848.148987\n",
       "52052  3795.25  3905.793235  3840.010681\n",
       "52053  3922.85  3907.105294  3852.755192\n",
       "52054  3879.04  3904.946425  3856.799009\n",
       "52055  3899.00  3904.489008  3863.291469\n",
       "52056  3934.21  3906.775238  3874.202012\n",
       "\n",
       "[46306 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Close', 'emafast1', 'emafast2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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